Adaptive rf system testing system and method

ABSTRACT

A system and method for testing an adaptive RF system in an emulated RF environment using a feedback control module to efficiently and accurately evaluate the performance of the RF system under test in the search space.

The present application claims the priority of U.S. Provisional PatentApplication Ser. No. 61/629,922 filed Dec. 1, 2011, the disclosure ofwhich is hereby incorporated by reference.

The present disclosure relates to the field of functional andperformance testing of adaptive or cognitive RF systems. Morespecifically, this disclosure describes a system and method forefficiently and effectively testing adaptive RF systems in thelaboratory where a methodology is used to produce field-quality validresults with a limited number of trial scenarios controllable in alaboratory environment.

BACKGROUND

Radio spectrum is scarce and the FCC, DoD and other internationalspectrum management organizations are constantly looking for ways tomore efficiently utilize this limited spectrum. Demand for spectrum iscontinuing to rise due to the explosive growth of data, voice,messaging, and video applications. One solution to meeting the need forimproved spectral efficiency as measured by bits/Hz/user is adaptiveradios (also referred to as dynamic spectrum access (DSA) or cognitiveradios (CR)). Adaptive radios can change their transmissioncharacteristics to maximize transmission capacity and coverage whileconserving spectral usage.

One of the challenges of deploying adaptive radio technology is that itcannot be fielded without comprehensive testing, and it cannot be testedin a densely RF system populated, live environment for fear ofpotentially interfering with existing spectrum users (primary users).Field testing is preferable to lab testing but requires a realisticenvironment where it can be verified that the System under Test (SUT)will not interfere with primary users or other spectrum users.Laboratory testing is more cost and schedule effective, repeatable,controllable and observable, but generally lacks in realism, especiallywith respect to RF environmental considerations.

There is an established and growing need to comprehensively test andevaluate performance of these new adaptive devices/systems in known andpostulated environments to establish behavior characteristics (“averagebehavior”) and reduce unintended field behavior risk (“abhorrent” or“rare event” behavior). Traditional test methods are increasinglystressed by the proliferation and diversity of the devices/systems andoperating environments. As used in this application, RF system includeRF devices, such as a transmitter, receiver or navigation device, aswell communication systems, navigation systems, radar systems or othersystems using transmitters or receivers. As used in this application,testing means evaluating input and output parameters for an RF systemacross a parameter search space in an RF environment in order todetermine the behavior characteristics of the RF system. Historically,RF system testing has fallen into two broad categories, field testingand laboratory simulation/testing. Field testing as illustrated in FIG.1 involves placing some number of devices in a realistic fieldenvironment and exercising them to test performance against specifiedfunctionality. Full-featured field tests place the wireless transceiversin a field scenario containing some representative RF environment wherethey will be operated while test data is collected. These sorts of testsare often expensive and complex to orchestrate, and can lack flexibilitysince mixes of test transceiver numbers/types/locations, incumbent RFuser numbers/types/locations and RF propagation conditions cannot besystematically varied to collect comprehensive data. FIG. 1schematically depicts a typical field test equipment setup. WirelessTransceiver Units Under Test (UUT) 100 operate in some RF environment110. The RF emissions are subject to the noise, path loss, multipathtransmission and interferers found in the local RF environment. Testinstrumentation 120 is established to measure the performance of the UUTand other PU of the RF environment. In order to accomplish a field testof this variety, the UUT must be physically located in the test RFenvironment, and test instrumentation must be constructed. In order tovary the numbers/types/locations of UUT and PU, physical units must beacquired and placed in the RF environment. In order to vary the RFenvironment, different field venues must be available. Additionally,test instrumentation must be provided and adapted for each UUT/PU/testenvironment scenario where testing is to be accomplished.

Many factors must be considered when selecting and configuring the fieldtest area including the specific type and host platform for the SystemsUnder Test (SUT), the characteristics and quantity of other RFinterferers in the environment, and environmental factors that affectthe radio propagation including terrain and morphology. Field testmethods have been viewed as the most realistic, but many growingchallenges limit their ability to be compelling. These challengesinclude:

-   -   Difficulty and complexity in testing high platform dynamic        systems    -   More devices/systems to test    -   More functionality & complexity to test including        adaptive/cognitive behavior    -   Test ranges require a broad set of realistic physical layouts    -   Requirements to emulate location-specific RF environments        including propagation and interferers    -   Requirements for conditions not realizable on test ranges        including prohibition by FCC rules    -   RF environment control difficult due to encroachment of        commercial RF sources.        All of above lead to increased costs, longer schedules, more        requirements on field test assets and ranges, and potentially        lower confidence in results. For adaptive RF systems, field        testing is not practical. Laboratory test methods are generally        more cost and schedule effective, are more controllable and        observable, but generally are lacking in realism, especially        with respect to RF environmental considerations.

There exist many variations of lab testing approaches, but they can begenerally bounded by “RF Path Simulator” and “Software Modeling”variants. The RF Path Simulator approach shown in FIG. 2, whichinterconnects RF systems/devices with conventional laboratory testequipment such as signal/noise generators, is only applicable to simpleRF environments, small numbers of devices/systems under test with simpleantenna systems, and small number of primary users/interferers.Lab-based testing using cable-based interconnection for RF emissions ofUUT and the RF environment is a prior art approach to testing toovercome the challenges of placing and monitoring devices in the fieldenvironment. FIG. 2 depicts a typical lab-based equipment setup. As infield testing, Wireless Transceiver Units Under Test (UUT) 100 areacquired and instrumented with Test Instrumentation 120. Instead of theRF environment being that found in the field, RF test equipment such assignal generators are used to produce Interferers 210, Noise Generators220, and Path Simulators 200 to simulate path loss and multipath in anRF channel. RF Interconnection 230 is accomplished using RF cables suchas coaxial cables. This test set up approach reduces some of thecomplexities of field testing, but introduces new concerns over RFenvironment realism. Further, it still requires the physicalintroduction of new UUT and RF test equipment into the configuration forcomprehensive transceiver configuration and RF environment results.

Traditional software modeling approaches as shown in FIG. 3 havehistorically made simplifications about the physical environment/radiopropagation effects, and generally cannot support any hardware in theloop (HITL) test cases. Their validity is therefore limited to a narrowgroup of test cases and not well suited to the adaptive RF system testproblem. A variation on RF cable-connected lab testing has become moreprevalent and straightforward as wireless transceiver devices havetended towards digital waveforms and digital hardware or softwareimplementation. FIG. 3 depicts a typical framework for modern wirelesscommunications devices as defined by the prior art OSI model. Here,different functions in the Wireless Transceiver 100 are allocated tolayers in the functional stack 300. The physical layer in stack 300 iswhere the waveform-related functionality is contained. The physicallayer can be segregated into a digital implementation portion 310 and ananalog portion 320. Typical functions in the digital transmit portion310 are waveform generation and digital to analog conversion. Typicalfunctions found in the analog portion 320 are baseband to RF conversion.Other digital processing functions associated with non-physical layers(2 through 7) are performed through digital data processing blocks 330.

A laboratory-based testing approach that combines the advantages of trueRF path/environment emulation and HITL, but implemented in the digitaldomain under software control, has the potential to deliver theadvantages of the different lab methods with the realism of fieldtesting. The test platform disclosed in commonly owned U.S. patentapplication Ser. No. 12/787,699, titled “Wireless Transceiver Test BedSystem and Method”, which is hereby incorporated by reference, followsthis approach. The present disclosure adds improvements directed to amethod to control the RF environment to execute a sufficient number oftest cases for validity and schedule the test cases so that a limitednumber are required for execution. This facet of the test bed problem isfurther described below.

Perhaps the most challenging part of adaptive RF system testing isaddressing the vast number of test cases that may have to be scheduledto comprehensively test an adaptive RF system. To illustrate themagnitude of the problem, an example test scenario for an adaptivenavigation receiver is presented. The test conditions can be groupedinto 5 categories, each with a large number of individual parameters asfollows:

-   -   1. GNSS Signals (# systems, # satellites, positions of        satellites, status of satellites (i.e. health, accuracy of        correction data, etc.))    -   2. Interference Signals (#, type, position, characteristics)    -   3. Augmentation (existence, types, characteristics of types        (including the following))        -   a. Other RF Source Augmentation (i.e. Signals of            Opportunity)        -   b. Mechanical Augmentation (i.e. IMU)        -   c. Correction Augmentation (i.e. WAAS)        -   d. Assist Augmentation (i.e. A-GPS)        -   e.    -   4. Propagation Environment (GNSS to PNT, Interference to PNT,        Augmentation to PNT (if applicable))    -   5. PNT System Configuration (host platform considerations        (varies by host platform)), orientation to sources (up to 6        degrees of freedom), # RF channels, antenna systems, user        configurable parameters.

It can be easily envisioned that the number of test cases couldroutinely reach into the millions (or higher for more complex RF systemtypes). Two challenges result from this condition. First, the time andassociated cost of performing the test may be prohibitive. Second, thevast amount of data produced by comprehensive testing may make usefulconclusions about the performance difficult or impossible to formulate.A desirable capability of the test asset would be a test methodologythat significantly reduced the number of tests run while maintaining thevalidity of the data (the ability to extract the performancecharacteristics of the RF system under test).

Based on a review of the available RF system test beds that exist inindustry and academia (including those referenced in U.S. patentapplication Ser. No. 12/787,699), a wireless transceiver test bedapproach, capable of efficiently producing valid performance test data,and yet is scalable, flexible and affordable is not known.

The present disclosure utilizes emerging technologies and trends in theareas of optimal search algorithms, digital signal processing, wirelessdevice design, wideband networks, computer and softwarearchitecture/capability and software-based modeling to provide a meansto address these shortcomings. Specific technology innovations thatcontribute to various aspects of the present disclosure include:

-   -   digital signal processing power and available algorithms and        models    -   ability to digitize RF with high fidelity    -   emerging software defined radio (SDR) software architectures,        such as SCA (Software Communications Architecture)    -   emerging commercial off-the-shelf digital radio and SDR        components (hardware and software)    -   ever increasing broadband connectivity between distributed sites    -   comprehensive and advanced RF propagation models    -   RF emitter models being built in software    -   proliferation of radio functionality being digital and        implemented in software with discrete events (bits, bursts,        frames, etc.).    -   standardization of baseband digitized interfaces to SDRs (such        as the VITA-49 Radio Transport Protocol)    -   optimal search algorithms including multi-queue branch and bound        algorithms.

The present disclosure is not limited to adaptive wireless devices inthe application area of communications, but broadly applies to allwireless devices and networks including receive only, transmit only anddiverse applications such as sensing, radar, and jamming. Further, it isnot limited to testing adaptive RF systems and could also be used toautomatically test conventional RF systems. The same properties ofeffectively and efficiently testing apply in that the test system wouldautomatically produce valid results using a reasonable number of RFenvironment scenarios.

SUMMARY OF DISCLOSURE

In one embodiment, the disclosed system uses a closed loop architecturethat creates a realistic radio environment allowing the adaptive radioor SUT to be fully stimulated under realistic and repeatable conditionsand then monitors how the SUT behaves and/or adapts. In thisarchitecture shown in FIG. 4, the total number of different testconditions are far too great to exhaustively test all combinations, i.e.number and type of emitters each with varying parameters (frequency,power level, modulation, etc.), different propagation paths consideringvarious terrain and morphology, different physical locations of the RFsystems, and stationary and mobile systems at different elevations. Thetotal number of test combinations could be in the many millions whichmakes exhaustive testing impractical.

To address this challenge, the architecture includes a feedback controlmodule 400 that uses the behavior, including adaptive behavior of the RFsystem under test (SUT) to intelligently tailor the RF environment inresponse to how the SUT 430 behaves in order to focus on RFenvironmental conditions that cause the radio to adapt. The adaptivenature of the system under test provides a means for the RF environmentto be dynamically changed during testing and does not require the use ofa priori information. For example, the RF environment module 420 cancontrol the number and type of emitters (including the system under testand interferers), the propagation characteristics taking into accountterrain and morphology, various physical layouts and the effects ofmovement of emitters and/or receivers. One objective of the controlsystem is to minimize the number of trials that are required to fullycharacterize the SUT 430 to a given confidence level.

The system under test 430 can be a physical system, or can beimplemented in software from instructions retrieved from memory (notshown) to emulate an actual RF system. RF environment module 420 isimplemented in software from instructions retrieved from memory (notshown) in response to control instructions from a feedback controlmodule 400. Test module 410 receives input from the RF environmentmodule 420 and RF system under test module 430. Test module 410 recordsobservations and makes measurements of the parameters associated withthe RF system under test 430 and stores them in a memory. Feedbackcontrol module 400 evaluates the test results of the test module 410 andmakes decisions and controls the RF environment by providing input to RFenvironment module 420. The feedback control module 400 can characterizethe adaptive performance of the system under test 430, and identifyaverage and rare-event behavior. Average behavior is defined as thedominant behavior or adaptation modes of the SUT. Included in thisdefinition would be the behavior exhibited by the SUT under typicalenvironmental conditions. Average behavior characterization may bedesirable when the objective is to understand the typical performancecharacteristics of an RF-based navigation device (i.e. GPS receiver).Rare-event behavior is defined as low probability behavior, typicallytriggered by unusual environmental circumstances. An example ofrare-event behavior would be when a cognitive radio becomes confused byunusual and rapidly changing spectrum conditions and begins to transmitin known primary user spectrum. Rare-event behavior characterization isvaluable to understand the likelihood of a cognitive radio creatinginterference for primary users.

The feedback control module 400 can use an optimization criteria todetermine an adequate characterization of the system under test usingthe minimum number of samples or trials. Further, feedback controlmodule 400 can determine the sufficiency of the evaluation and the needfor further testing suing a confidence level, costing function, or otherobjective criteria. In one embodiment, the feedback control module 400can determine a gradient between parameters across the search space. Thegradient between parameters can be used to divide the search space intoa plurality of subspaces and the feedback control module can control theRF environment module to direct further testing on only specifiedsubspaces that provide the most useful information.

In one embodiment, characterization is achieved by varying the RFenvironment in which the SUT operates in order to characterizeperformance across this “search space”. The algorithm includes theconcept of scheduling in the sense that tests will need to be performedon the SUT in a time-serial fashion, and test cases (differentcombinations of environment and SUT configuration parameters) can bescheduled in different orders/combinations by the algorithm to achievesome goal. A brute force approach would be to vary each parameter acrossall combinations to measure performance. This is at least inefficientand likely not practical as the number of parameters and range of valuesis extremely large.

Returning to the purpose of the scheduling algorithm, efficiency can bedefined as minimizing the “cost” of performing the testing. The totalcost can be defined as the total time to do the testing. The time to dothe testing can be defined as the number of tests times the time to do aparticular test. The time to do a test will depend on which environmentor SUT configuration parameters are in play. For example, changing amechanical parameter related to the inertial measurement unit (IMU) inan adaptive navigation device will likely take longer than an RFparameter such as changing an attenuation value. The equation for costis defined as C=Σ_(n=1) ^(N)T(n_(X)), where C is the total cost (ortime), n is the trial number, N is the total number of trials, andT(n_(X)) is the time required for a specific trial given the set of Xindependent variables. Validity of the results can be defined as resultsthat provide the same information as if the entire parametric space(environment variables and SUT configuration variables) were tested. Inthis case, the goal is to test a subset of all possible test cases, andbe “confident” that it represents the useful information contained inthe exhaustive test case.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a simplified block diagram illustrating the components in atypical prior art field-based testing configuration for an RF device orsystem.

FIG. 2 is a simplified block diagram illustrating the components in atypical prior art RF interconnected laboratory-based testingconfiguration for an RF device or system.

FIG. 3 is a simplified block diagram illustrating the components in atypical prior art software model-based testing configuration for an RFdevice or system.

FIG. 4 is a simplified block diagram illustrating one embodiment of afeedback control algorithm to adjust the RF environment based on SUTbehavior.

FIG. 5 is a simplified graphical illustration of information regions forone embodiment of a simple one variable case.

FIG. 6 illustrates a simplified pictorial representation of oneembodiment of a branch and bound algorithm applied to a timing-basednavigation application.

FIG. 7 illustrates a simplified pictorial representation of oneembodiment of a branch and bound algorithm applied to a timing-basednavigation application in three dimensions.

FIG. 8 illustrates a simplified flow diagram of the operation of oneembodiment of the present disclosure.

DETAILED DESCRIPTION

An insightful description for the adaptive RF system testing methodologyto test a subset of all possible test cases, and be “confident” that itrepresents the useful information contained in the exhaustive test case.Returning to the navigation device test scenario provides a means toillustrate this further in one embodiment of the system. As an example,suppose the dependent variable (what performance is being measured atthe SUT) is GPS pseudo-range for a satellite, and the independentvariable is S/N ratio at the SUT antenna. Assuming the navigation deviceis attempting to acquire the C/A code using a 10 ms coherent integrationperiod, we know that at S/N ranges of approximately −30 dB S/N or lesswill be unreliable. Conversely, at S/N values of −20 dB and above shouldbe very reliable. Across these low and high ranges, there is littleinformation provided in taking multiple measurements. Of course, betweenthese ranges (−30 to −20 dB S/N) there is a great deal of informationabout the performance of the SUT because the dependent variable ischanging rapidly, and highly granular measurements are appropriate. Thisconcept is shown in FIG. 6.

In one embodiment, a method of detecting rapidly changing dependentvariable regions 500 (vs. relatively static regions 510, 520) is somemeasure of variance of samples across the region. In this application,the term gradient is used to describe the varying relationship orvariance between parameters across a search space. The gradient can beused to determine high information and low information areas. It istherefore a goal of the cognitive scheduling algorithm to schedule testsin these “high information regions” 500 and not test in “low informationregions” 510, 520 to produce “validity data”. Performing tests in the“low information regions” 510, 520 can be thought of as generatingredundant data, and performing tests in the “high information regions”500 can be thought of as generating non-redundant data. The embodimentin FIG. 5 illustrates a one dimensional test case, and clearly theresults apply to multi-dimensional test cases (independent and dependentvariables are vectors) with associated exponential cost reductionpotential. The problem can be mathematically formulated as follows. Thevector Y is a function of N observable dependent variable outputs y_(r),(accuracy in horizontal and vertical position, speed, time to first fix,etc.)

={y₁, y₂, . . . y_(N)}

Each output y_(n) is a function M independent variable inputs x_(m)which include environmental effects such as noise power, interferencepower, direction of interference, terrain, etc. In other words,

${Y = \begin{bmatrix}{y_{1}(X)} \\\vdots \\{y_{N}(X)}\end{bmatrix}},{{{where}\mspace{14mu} X} = \left\{ {x_{1},x_{2},{\ldots \mspace{14mu} x_{M}}} \right\}}$

For each output y_(n)(X), the point of maximum “value”, or X_(max) inour tests is arg

$\left\{ {\max_{X}{\frac{\partial y_{n}}{d\; X}}^{2}} \right\}.$

Also, for each y_(n)(X), the “range” of values within X can beidentified as lying between:

${X_{low} = {\arg \left\{ {g_{n|_{X < {g_{n}{(X_{\max})}}}} < ɛ} \right\}}},{{{and}\mspace{14mu} X_{high}} = {\arg \left\{ {g_{n|_{X > {g_{n}{(X_{\max})}}}} < ɛ} \right\}}},{{{where}\mspace{14mu} {g_{n}(X)}} = {\frac{\partial y_{n}}{d\; X}}^{2}}$

and ε is the threshold in which change in the out value g_(n) isnegligible.

The goal of the optimization is then to converge on a set of testconfigurations that adequately span the ranges of X_(low) and X_(high)with respect to each of the observable outputs y_(n). We also wish tofurther constrain the optimization with two conditions: 1) to avoidduplication of values within X such that the process does not needlesslyrequire repetition of the same set of environment configurations whichwastes testing time, and 2) to minimize the “cost” of the overalltesting in terms of the previously define cost C=Σ_(n=1) ^(N)T(n_(X)).This leads to the need for an algorithm, or mathematical formulation,and an implementation approach (software based framework) to make thecorrect scheduling priority decisions. The following discussionsdescribes one embodiment to provide a candidate mathematical formulationbased on branch and bound search algorithms, and an implementationapproach based on an expert system. Mathematical formulations can beconstructed to address different categories of relationships between thedependent and independent variables including deterministic andstatistical. They can also be constructed to accommodate RF systemsunder test that exhibit behavior based on current stimuli plus paststimuli (i.e. systems with memory). Implementation approaches can makedifferent assumptions about a priori knowledge of the RF system undertest. For example, the RF system under test can be viewed as a “blackbox” where there is no a priori knowledge, or a “gray box” where someknowledge of behavior is known, but not with precision. An example of“gray box” knowledge would be that location accuracy of a navigationdevice improves as a function of increased navigation satellite signalto noise ratio. The existence of a priori knowledge can be used to guidethe search algorithms with respect to parameter value ranges andgranularities.

Many variations of the Branch and Bound algorithm have been proposed ina wide variety of application fields to solve different types of searchproblems. The adaptive RF system test problem is somewhat different thanmost applications of Branch and Bound in that most applications areattempting to efficiently find a point in n-space (vector of dimensionn) that meets some criteria for optimality. Adaptive RF system testingis attempting to efficiently span n-space to find all of the points(vectors of dimension n) that are needed to accurately describe theresults of testing all of n space. In this embodiment, the Branch andBound approach has been adapted to apply to this problem. One key changeis that all of the results of the testing through the search processform the desired result vs. just the “final” point in space.

The basic approach is to envision an n-dimensional search volume whichis divided up into some number of subspaces over time. The algorithmiteratively decides which subspaces to further divide, and which todiscard with respect to future action. For the adaptive RF system testapplication, the search spaces of interest are those where the resultsof test cases vary within the space (for example, where a small changein S/N creates a large change in the pseudo-range timing measurement),and the search spaces of interest are those where the results of thetest cases do not vary (changes in S/N produce little change inpseudo-range time measurement). In the context of the branch and boundalgorithm vernacular, splitting search spaces is branching, computingthe variability of the test results in bounding, and deciding not tofurther split a search space is pruning. This process is shown in FIG.6.

Referring to FIG. 6, the n-dimensional search space is shown as a cubeon far left 600. Five major dimensions may be defined for this space,with each dimension have multiple sub-dimensions. For illustrationpurposes, using the S/N-pseudo-range time measurement example, S/N isvaried along the X-axis 610. The measured (dependent) variable ispseudo-range timing accuracy along the Y-axis 620. The branch and boundalgorithm would make some number of measurements across the dimension,shown as red dots on the far right cube. The algorithm would split thedata into sub-intervals (branch, see center cube 640), then calculatesome measure of variability (such as variance) for data in sub-intervalsacross the dimension (calculated on the solid black and diagonally lineddots in each of the two sub-intervals in the center cube). It would thentest the results to see if any sub-interval is to be discarded withrespect to future testing (bounding) 650. For example, the sub-intervalon the right side of the center cube has been discarded (pruned) 660 forfurther testing. The algorithm would then make more measurements in thesub-intervals that have not been pruned (shown as crossed hatched dotsin the center cube 670). The process repeats as shown in the far rightcube. Here, the left half interval of the cube face has been divided(branched) 680, the measurements in each resultant sub-interval havebeen tested (bounded) 685, and the right sub-interval has been discarded(pruned) 690. The horizontally lined measurement dots 695 are thebeginning of the next iteration.

This simple illustration uses only one dimension in the n-dimensionalspace. It also assumes the x axis is cardinal, and shows the measurementresults are monotonically increasing (which may or may not be true).Relaxation of these simplifications (along with many others) make thesearch problem more complex. FIG. 7 shows how the Branch and Boundalgorithm might operate in 3 dimensions (still a very simple example).Note that on each face of the cube (each dimension 700, 710, 720),decisions are made on how to branch and prune.

Clearly when working in n-space, a joint metric of measurement resultvariability would be used, and the cost functions previously describedwould be used in addition to the variability in the bounding step tomake pruning decisions and the next branching decisions. One embodimentaddresses these conditions, and is based on a branch and bound algorithmthat has been described in terms of solving biological problemsinvolving intractable (NP-hard) problems [1]. It uses a multi-queuebranch and bound algorithm, and possesses many features including:

-   -   Completeness-guaranteed to find an optimal solution if it exists    -   Optimality-provides global optimum if allowed to run to        completion    -   Anytime-can be stopped at any time and provides a useful result    -   Irredundant-will find a local minima/maxima only once    -   Allows admissible heuristics-behavior can be influenced if a        priori knowledge about search space exists.        The multi-queue feature is very important for n-dimensional        problems and allows multiple subspace (referred to as        hyper-rectangles) tests cases to be managed and prioritized        through the search process.

The branch and bound embodiment is one of many algorithm alternativesthat can be used for the control algorithm function. Algorithms that aredesigned to estimate average behavior or evaluate rare-eventprobabilities may be used to detect abhorrent behavior includingaccommodating variables that are probabilistic vs. deterministic withstochastic modeling.

In one embodiment, the ability of the algorithm to accept a prioriknowledge (or learned) is also a key feature. The scheduling of testcases can be influenced by the current knowledge of how adaptive RFsystems react to different test stimuli, and by information that islearned as different systems or over time as the subject SUT are tested.The degree to which a priori (or learned knowledge is available to thecontrol algorithm can be categorized as “white”, “gray” and “black” boxtesting. The “box” refers to the SUT, and “white” means that internalcontrol algorithms for the box are completely known, “gray” means theyare partially known, and “black” means they are unknown. For example, ifit is know that the test results vary in a monotonically increasing wayto ordered test stimuli changes (“white” or “gray” case), then thebranch process may be able to be applied more efficiently than if thefunction shape is unknown or with many local maximums and minimums. Thislearning process may be automated given some of the machine learning(with and without a human in the loop) algorithms that exist in thecurrent art.

The Branch and Bound class of algorithms, as well as other controlalgorithm embodiments, can be implemented using either a procedural orinferential approach. For the procedural case, simple IF-THEN-ELSEconstructs would be used to adapt the behavior of the UE based onmeasured quantities. For the inferential case, a set of complex ruleswould be constructed based on expert knowledge to adapt the algorithmbehavior. The rules would be scheduled and tested using an inferenceengine as a function of measurements being made and other conditions inthe test fixture. Procedural approaches provide a good solution when thenumber of measurements and adaptation options are small and unambiguous.Inference engine approaches using rule bases provide a better solutionwhen the combinations of measurements, static conditions and adaptationalternatives become large and unwieldy.

FIG. 8 shows a simplified flow chart of one embodiment of the presentdisclosure in operation. In step 800, a number of parameters can beidentified to evaluate the testing of the RF system across the searchspace in an emulated RF environment. The RF environment is emulated insoftware, which allows for changes to be made to the RF environmentduring testing. In step 810, the testing of the RF system may beaccomplished by evaluating the identified parameters across the initialsearch space. In step 820, a gradient can be determined representing thevarying relationship between the identified parameters. The gradient canbe used to define areas of high information and distinguish them fromareas of low information. Generally, the larger the gradient, the morevariance between the parameters, while a lower gradient reflects a lowerdynamic relationship between the parameters. For lower gradient regions,less testing is required of this subspace because the test samplesprovide redundant information due to the lower varying relationshipbetween the tested parameters. On the other hand, subspaces associatedwith a larger gradient require more test samples in order to accuratelydescribe the more dynamic relationship between the parameters. One goalof this embodiment is to determine areas of high information and directfurther testing to the subspace associated with this high informationarea. Thus, in step 830, the gradient can be used to divide the searchspace into subspaces. The subspaces can then be evaluated to determinewhich subspaces have high information and then continue testing only inthese subspaces that are associated with high information. Therefore, instep 840, the gradient of each subspace can be compared to apredetermined threshold, and if the gradient is less than thepre-determined threshold, that subspace can be eliminated from furthertesting. Effectively, this means that the testing that was previouslyconducted in this subspace was sufficient to accurately predict theresponse of the RF System under test in this emulated RF environment andfurther testing will only produce redundant results. Thus, testresources can be devoted to testing other subspaces which are expectedto provide more useful information. The predetermined threshold can beselected such that further testing of the subspace can be expected toprovide redundant information, For example, the predetermined thresholdcan represent a gradient where the expected test results would fallwithin a measure of standard deviation, or the predetermined thresholdcan represent a gradient selected based on a prior knowledge of therelationship between the tested parameters. Once the subspaces havinghigh information are identified, further testing of the subspaces cancontinue in step 850. Testing can continue until a completion criteriais met 860. In one embodiment, the completion criteria can be when theconfidence level of the results exceeds some predetermined threshold.The confidence level can be defined as the level of confidence that thetest results thus far adequately define the relationship between theparameters such that additional testing is not required. In anotherembodiment, the completion criteria can be based on a costing functionas described above. If the completion criteria is not satisfied 865, thegradient of the new test results can be determined as in step 820 andcan be used to further limit the search space as steps 830-860 arerepeated until the completion criteria is met. [0030] It may beemphasized that the above-described embodiments, particularly any“preferred” embodiments, are merely possible examples ofimplementations, merely set forth for a clear understanding of theprinciples of the disclosure. Many variations and modifications may bemade to the above-described embodiments of the disclosure withoutdeparting substantially from the spirit and principles of thedisclosure. All such modifications and variations are intended to beincluded herein within the scope of this disclosure and the presentdisclosure and protected by the following claims Embodiments of thesubject matter and the functional operations described in thisspecification can be implemented in digital electronic circuitry, or incomputer software, firmware, or hardware, including the structuresdisclosed in this specification and their structural equivalents, or incombinations of one or more of them. Embodiments of the subject matterdescribed in this specification can be implemented as one or morecomputer program products, i.e., one or more modules of computer programinstructions encoded on a tangible program carrier for execution by, orto control the operation of, data processing apparatus. The tangibleprogram carrier can be a propagated signal or a computer readablemedium. The propagated signal is an artificially generated signal, e.g.,a machine-generated electrical, optical, or electromagnetic signal thatis generated to encode information for transmission to suitable receiverapparatus for execution by a computer. The computer readable medium canbe a machine-readable storage device, a machine-readable storagesubstrate, a memory device, a composition of matter affecting amachine-readable propagated signal, or a combination of one or more ofthem.

The term “circuitry” encompasses all apparatus, devices, and machinesfor processing data, including by way of example a programmableprocessor, a computer, or multiple processors or computers. Thecircuitry can include, in addition to hardware, code that creates anexecution environment for the computer program in question, e.g., codethat constitutes processor firmware, a protocol stack, a databasemanagement system, an operating system, or a combination of one or moreof them.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, or declarative orprocedural languages, and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, or other unitsuitable for use in a computing environment. A computer program does notnecessarily correspond to a file in a file system. A program can bestored in a portion of a file that holds other programs or data (e.g.,one or more scripts stored in a markup language document), in a singlefile dedicated to the program in question, or in multiple coordinatedfiles (e.g., files that store one or more modules, sub programs, orportions of code). A computer program can be deployed to be executed onone computer or on multiple computers that are located at one site ordistributed across multiple sites and interconnected by a communicationnetwork.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read only memory ora random access memory or both. The essential elements of a computer area processor for performing instructions and one or more memory devicesfor storing instructions and data. Generally, a computer will alsoinclude, or be operatively coupled to receive data from or transfer datato, or both, one or more mass storage devices for storing data, e.g.,magnetic, magneto optical disks, or optical disks. However, a computerneed not have such devices. Moreover, a computer can be embedded inanother device, e.g., a mobile telephone, a personal digital assistant(PDA), a mobile audio or video player, a game console, a GlobalPositioning System (GPS) receiver, to name just a few.

Computer readable media suitable for storing computer programinstructions and data include all forms of non volatile memory, mediaand memory devices, including by way of example semiconductor memorydevices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks,e.g., internal hard disks or removable disks; magneto optical disks; andCD ROM and DVD-ROM disks. The processor and the memory can besupplemented by, or incorporated in, special purpose logic circuitry.

To provide for interaction with a user, embodiments of the subjectmatter described in this specification can be implemented on a computerhaving a display device, e.g., a CRT (cathode ray tube) or LCD (liquidcrystal display) monitor, for displaying information to the user and akeyboard and a pointing device, e.g., a mouse or a trackball, by whichthe user can provide input to the computer. Other kinds of devices canbe used to provide for interaction with a user as well; for example,input from the user can be received in any form, including acoustic,speech, or tactile input.

Embodiments of the subject matter described in this specification can beimplemented in a computing system that includes a back end component,e.g., as a data server, or that includes a middleware component, e.g.,an application server, or that includes a front end component, e.g., aclient computer having a graphical user interface or a Web browserthrough which a user can interact with an implementation of the subjectmatter described is this specification, or any combination of one ormore such back end, middleware, or front end components. The componentsof the system can be interconnected by any form or medium of digitaldata communication, e.g., a communication network. Examples ofcommunication networks include a local area network (“LAN”) and a widearea network (“WAN”), e.g., the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

While this specification contains many specifics, these should not beconstrued as limitations on the scope of any invention or of what may beclaimed, but rather as descriptions of features that may be specific toparticular embodiments of particular inventions. Certain features thatare described in this specification in the context of separateembodiments can also be implemented in combination in a singleembodiment. Conversely, various features that are described in thecontext of a single embodiment can also be implemented in multipleembodiments separately or in any suitable sub-combination. Moreover,although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to asub-combination or variation of a sub-combination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems cangenerally be integrated together in a single software product orpackaged into multiple software products.

What is claimed:
 1. A method of testing of an RF system in a emulated RFenvironment comprising the steps of: (a) identifying at least twoparameters to evaluate for an RF system, wherein a first parameter is anindependent variable and a second parameter is a dependent variable; (b)testing the RF system by evaluating the at least two parameters across afirst search space in a simulated RF environment; (c) determining agradient between the first and second parameters across the first searchspace; (d) dividing the search space into a plurality of subspaces as afunction of the determined gradient; (e) eliminating a subspace fromfurther testing if the determined gradient for the subspace is less thana predetermined threshold; (f) testing the identified at least twoparameters in the remaining subspaces that have not been eliminated; (g)continuing testing the identified at least two parameters in theremaining subspaces until a completion criteria is met.
 2. The method ofclaim 1 wherein the RF system is at least one of a transmitter, areceiver, a communication network a navigation device a system or aradar system.
 3. The method of claim 1 wherein the gradientpredetermined threshold is determined as a function of a prioriknowledge of the first and second parameter relationship,
 4. The methodof claim 1 wherein the completion criteria is a costing function.
 5. Themethod of claim 1 wherein the completion criteria is a confidence level.6. The method of claim 1 wherein the first or second parameter includesa noise component.
 7. The method of claim 1 wherein the first parameteris a signal to noise ratio and the second parameter is timing accuracy.8. The method of claim 1 wherein three parameters are identified andwherein step (c) determines a gradient between the three parametersacross the first search space.
 9. The method of claim 1 wherein the RFenvironment is emulated in software and capable of being changed whilethe at least two parameters are tested.
 10. The method of claim 1wherein the RF system includes a memory containing previous testinformation including values for at least one of the parameters in thetested RF environment
 11. A system for testing of an RF system in aemulated RF environment, comprising: a memory for storing computerreadable code; an RF environment module operatively coupled to thememory, the module configured to emulate an RF environment in responseto control instructions from a feedback control module; an RF systemunder test operatively coupled to the memory; a test module operativelycoupled to the memory, the module configured to test the RF system byevaluating at least two parameters across a first search space in anemulated RF environment, wherein a first parameter is an independentvariable and a second parameter is a dependent variable; a feedbackcontrol module operatively coupled to the memory, the module configuredto: determine a gradient between the first and second parameters acrossthe first search space; divide the search space into a plurality ofsubspaces as a function of the determined gradient; eliminate a subspacefrom further testing if the determined gradient for the subspace is lessthan a predetermined threshold; determine if a completion criteria hasbeen met, and if the completion criteria has not been met, control theRF environment module to provide the remaining subspaces for furthertesting.
 12. The system of claim 11 wherein the at least two parametersis selected from the set of RF environment parameters configurable inthe RF environment module and RF system under test parameters observablein the RF system under test.
 13. The system of claim 11 wherein thecompletion criteria is a function of a confidence level.
 14. The systemof claim 11 wherein the completion criteria is a costing function. 15.The system of claim 11 wherein the RF environment module emulates thepropagation characteristics for a given terrain and morphology.
 16. Thesystem of claim 11, wherein the RF system under tested is implemented insoftware configured to emulate an actual RF system.